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(genetics) the process of locating genes on a chromosome (同)chromosome_mapping
a datum that can be represented numerically
of or relating to statistics; "statistical population"
of or relating to or in terms of a parameter; "parametric equation"

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(数学で)関数 / 地図製作 / (時間の)割当て
統計値,統計量
統計の,統計学上の

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出典(authority):フリー百科事典『ウィキペディア（Wikipedia）』「2017/08/23 02:09:54」(JST)

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Statistical parametric mapping or SPM is a statistical technique created by Karl Friston for examining differences in brain activity recorded during functional neuroimaging experiments using neuroimaging technologies such as fMRI or PET. It may also refer to a specific piece of software created by the Wellcome Department of Imaging Neuroscience (part of University College London) to carry out such analyses.

The statistical parametric mapping approach

Unit of measurement

Functional neuroimaging, one type of 'brain scanning', involves the measurement of brain activity. The specific technique used to measure brain activity depends on the imaging technology being used (see fMRI and PET for examples). Regardless of which technology is used, the scanner produces a 'map' of the area being scanned that is represented as voxels. Each voxel typically represents the activity of a particular coordinate in three-dimensional space. The exact size of a voxel will vary depending on the technology used, although fMRI voxels typically represent a volume of 27 mm³ (a cube with 3mm length sides).

Experimental design

Researchers are often interested in examining brain activity linked to a specific psychological process or processes. An experimental approach to this problem might involve asking the question 'which areas of the brain are significantly more active when a person is doing task A compared to task B?'. Although each task might be designed to be identical, except for the aspect of behaviour under investigation, the brain is still likely to show changes in activity between tasks due to factors other than task differences (as the brain is involved with co-ordinating a whole range of parallel functions unrelated to the experimental task). Furthermore, the signal may contain noise from the imaging process itself.

To accommodate these random effects, and to highlight the areas of activity linked specifically to the process under investigation, statistics are used to look for the most significant difference above and beyond background brain activity. This involves a multi-stage process to prepare the data, and to subsequently analyse it using a statistical method known as the general linear model.

Image pre-processing

Images from the brain scanner may be pre-processed before any statistical comparison takes place to remove noise or correct for sampling errors.

A study will usually scan a subject several times. To account for the motion of the head between scans, the images will usually be adjusted so each of the voxels in the images corresponds (approximately) to the same site in the brain. This is referred to as realignment or motion correction, see image realignment.

Functional neuroimaging studies usually involve several participants, who will have slightly differently shaped brains. All are likely to have the same gross anatomy, but there will be minor differences in overall brain size, individual variation in topography of the gyri and sulci of the cerebral cortex, and morphological differences in deep structures such as the corpus callosum. To aid comparisons, the 3D image of each brain is transformed so that superficial structures line up, a process known as spatial normalization. Such normalization typically involves not only translation and rotation, but also scaling and nonlinear warping of the brain surface to match a standard template. Standard brain maps such as the Talairach-Tournoux or templates from the Montréal Neurological Institute (MNI) are often used to allow researchers from across the world to compare their results.

Images are often smoothed (similar to the 'blur' effect used in some image-editing software) by which voxels are averaged with their neighbours, typically using a Gaussian filter or by wavelet transformation, to make the data less noisy.

Statistical comparison

Parametric statistical models are assumed at each voxel, using the general linear model to describe the variability in the data in terms of experimental and confounding effects, and residual variability. Hypotheses expressed in terms of the model parameters are assessed at each voxel with univariate statistics.

Analyses may also be conducted to examine differences over a time series (i.e. correlations between a task variable and brain activity in a certain area) using linear convolution models of how the measured signal is caused by underlying changes in neural activity.

Because many statistical tests are being conducted, adjustments have to be made to control for Type I errors (false positives) potentially caused by the comparison of levels of activity at a large number of voxels. In this case, a Type I error would result in falsely detecting background brain activity as activity related to the task. Adjustments are made, based on the number of resels in the image and the theory of continuous random fields in order to set a new criterion for statistical significance that adjusts for the problem of multiple comparisons.

Graphical representations

Brain activation from fMRI shown as patch of colour on MRI scan

Differences in measured brain activity can be represented in a number of ways.

Most simply, they can be presented as a table, displaying coordinates that show the most significant differences in activity between tasks. However, differences in brain activity are more often shown as patches of colour on an MRI brain 'slice', with the colours representing the location of voxels that have shown statistically significant differences between conditions. The gradient of color is mapped to statistical values, such as t-values or z-scores. This creates an intuitive and visually appealing means of delineating the relative statistical strength of a given area of activation. Recently, an alternative approach has been suggested, in which the statistical map is combined with the map of the original difference in brain activity (or, more generally speaking, with the original contrast) and colorcodes are attributed to the latter.

Differences in activity may also be represented as a 'glass brain', a representation of three outline views of the brain as if it were transparent. Only the patches of activation are visible as areas of shading. This is useful as a quick means of summarizing the total area of significant change in a given statistical comparison.

SPM software

SPM is software written by the Wellcome Department of Imaging Neuroscience at University College London to aid in the analysis of functional neuroimaging data. It is written using MATLAB and is distributed as free software.

External links

Wikibooks SPM Wikibook.
fMRI guide by Chris Rorden
Introduction to fMRI: experimental design and data analysis
SPM software and documentation from the Wellcome Department of Imaging Neuroscience.
Cambridge Imagers - Neuroimaging information and tutorials.
Buttons in SPM5 PowerPoint presentation from the SPM for dummies course
SPM-Information to include in papers
ISAS (Ictal-Interictal SPECT Analysis by SPM) - Yale University
AutoSPM: Automated SPM for Surgical Planning

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English Journal

Voxel-based mapping of grey matter volume and glucose metabolism profiles in amyotrophic lateral sclerosis.

Buhour MS1, Doidy F1, Mondou A1, Pélerin A1, Carluer L1, Eustache F1, Viader F1, Desgranges B2.
EJNMMI research.EJNMMI Res.2017 Dec;7(1):21. doi: 10.1186/s13550-017-0267-2. Epub 2017 Mar 6.
PMID 28266002

18F-fallypride PET-CT of dopamine D2/D3 receptors in patients with sporadic amyotrophic lateral sclerosis.

Fu X1, Zhu W2, Guo Z3, Shu G4, Cui F5, Yang F5, Zhang Y1, Ren Y5, Zhang X6, Zhang X5, Chen Z5, Ling L5, Huang X7, Zhang J6.
Journal of the neurological sciences.J Neurol Sci.2017 Jun 15;377:79-84. doi: 10.1016/j.jns.2017.03.013. Epub 2017 Mar 10.
PMID 28477714

Myoelectric Activation Pattern Changes in the Involved Limb of Individuals With Transtibial Amputation During Locomotor State Transitions.

Nakamura BH1, Hahn ME2.
Archives of physical medicine and rehabilitation.Arch Phys Med Rehabil.2017 Jun;98(6):1180-1186. doi: 10.1016/j.apmr.2016.12.003. Epub 2017 Jan 3.
PMID 28057517

Japanese Journal

Cognitive functions in Parkinson's disease: Relation to disease severity and hallucination

Wakamori Takaaki,Agari Takashi,Yasuhara Takao,Kameda Masahiro,Kondo Akihiko,Shinko Aiko,Sasada Susumu,Sasaki Tatsuya,Furuta Tomohisa,Date Isao
Parkinsonism and Related Disorders 20(4), 415-420, 2014-04
… We used Mini-Mental State Examination (MMSE) and Wechsler Adult Intelligence Scale-Third edition (WAIS-III) to evaluate cognitive functions, and three-dimensional stereotactic ROI template (3DSRT) and Statistical Parametric Mapping (SPM) 8 to evaluate single photon emission CT (SPECT) recordings of regional CBF. …
NAID 120005473074

最適閾値設定による頭部MRI FreeSurfer解析の処理精度向上(ポスター発表2,計算解剖モデルに基づく診断・治療支援,医用画像処理一般)

田中美香,石井賢二,坂田宗之,石渡喜一
電子情報通信学会技術研究報告. MI, 医用画像 113(410), 309-313, 2014-01-19
… が,解析結果は解析対象画像の画質の影響を受ける.本研究では皮質と白質のコントラストが比較的低い3D-SPGR(3次元T1強調画像)シーケンスにより撮像された画像の解析において,SPM (Statistical Parametric Mapping)を利用した閾値設定を追加することで解析結果の向上が図れるかどうかについて検討した.提案手法による解析結果とFreeSurfer標準解析結果を比較したところ,健常例・認知症症例 …
NAID 110009821285

Vector field statistical analysis of kinematic and force trajectories

Pataky Todd C,Robinson Mark A,Vanrenterghem Jos
JOURNAL OF BIOMECHANICS 46(14), 2394-2401, 2013-09-27
… The method, called 'statistical parametric mapping' (SPM), uses random field theory to objectively identify field regions which co-vary significantly with the experimental design. …
NAID 120005458451

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リンク元	「SPM」「統計的パラメトリックマッピング」
関連記事	「statistic」「statistical」「mapping」「parametric」「parametrics」